(subjectif, tbc)

Tutorials

1. Langford, Structured prediction. http://www.hunch.net/~l2s/

Invited talk: Bottou +++

1. 2 learning modules in interaction (one explores, one classifies): misleading effects (exploration results are bad on average, therefore no need to explore, we were right the first time)
2. test: must be reconsidered. (consider the tails of distribution and coverage)

Papers

1. Unsupervised Domain Adaptation by Backpropagation Yaroslav Ganin, Victor Lempitsky
   1. two objectives on features: being discriminative wrt class; not discriminant wrt source/target
2. Learning Transferable Features with Deep Adaptation Networks Mingsheng Long, Yue Cao, Jianmin Wang, Michael Jordan
   1. related to the previous + kernels.
3. Strongly Adaptive Online Learning Amit Daniely, Alon Gonen, Shai Shalev-Shwartz
   1. Online and adaptive weights on experts + intervals + doubling trick
4. Adaptive Belief Propagation Georgios Papachristoudis, John Fisher
   1. Question for me: why not considering several spanning trees...
5. Weight Uncertainty in Neural Network Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
   1. weight = Gaussian; + Bayes by backprop (Graves).
6. Gradient-based Hyperparameter Optimization through Reversible Learning, Dougal Maclaurin, David Duvenaud, Ryan Adams
   1. derivative of misclassification wrt hyper-parameters.
7. On Symmetric and Asymmetric LSHs for Inner Product Search Behnam Neyshabur, Nathan Srebro
   1. Different random projections for queries and for solutions.
8. The Ladder: A Reliable Leaderboard for Machine Learning Competitions Avrim Blum, Moritz Hardt
   1. validation set, test set, multiple trials.
9. Learning to Search Better than Your Teacher Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume, John Langford
   1. ??
10. Learning Fast-Mixing Models for Structured Prediction Jacob Steinhardt, Percy Liang

??

Papers where I think one could do otherwise

1. On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments Yifan Wu, Andras Gyorgy, Csaba Szepesvari
   1. MCTS ?

Papers where I must have missed something (because otherwise, ...)

1. Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection Julie Nutini, Mark Schmidt, Issam Laradji, Michael Friedlander, Hoyt Koepke
   1. not compared to BFGS !!!

Papiers anciens que j'avais manqués

1. Label-Embedding for Attribute-Based Classification (embarrassingly simple).

Ce qui peut interesser...

Cécile: (rapport avec la thèse de Dawei).
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments
Yifan Wu, Andras Gyorgy, Csaba Szepesvari